CN114460918B - Method, device, equipment and storage medium for detecting equipment - Google Patents
Method, device, equipment and storage medium for detecting equipment Download PDFInfo
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- CN114460918B CN114460918B CN202111657118.8A CN202111657118A CN114460918B CN 114460918 B CN114460918 B CN 114460918B CN 202111657118 A CN202111657118 A CN 202111657118A CN 114460918 B CN114460918 B CN 114460918B
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- 238000004590 computer program Methods 0.000 claims description 8
- 238000001514 detection method Methods 0.000 abstract description 35
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- 238000012549 training Methods 0.000 description 8
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The present disclosure relates to a detection method, apparatus, device, and storage medium for a device. The method comprises the following steps: acquiring a plurality of target operation parameters of equipment to be detected; and inputting the plurality of target operation parameters into a target model to obtain target operation state information of the equipment to be detected, wherein the target operation state information comprises target operation state and fault position information. The method can improve the detection efficiency of the equipment.
Description
Technical Field
The disclosure relates to the technical field of detection, and in particular relates to a detection method, a detection device and a storage medium of equipment.
Background
Currently, in order to detect the operation state of industrial equipment, the state parameters of the industrial equipment need to be analyzed and detected, usually, equipment operation and maintenance personnel with abundant experience enter the field to detect and calculate the state parameters of the equipment one by one in the field, so as to determine the operation state of the industrial equipment.
However, with the above method, the time required for manual detection is relatively long, resulting in lower detection efficiency of the device operation state.
Disclosure of Invention
The disclosure provides a detection method, a detection device and a storage medium method for equipment, which can improve the detection efficiency of the equipment.
In a first aspect, the present disclosure provides a method for detecting a device, including:
acquiring a plurality of target operation parameters of equipment to be detected;
and inputting the plurality of target operation parameters into a target model to obtain target operation state information of the equipment to be detected, wherein the target operation state information comprises target operation state and fault position information.
Optionally, the inputting the plurality of target operation parameters to a target model to obtain target operation state information of the device to be detected includes:
inputting the target operation parameters into the target model to obtain a plurality of sub-target operation states, wherein each sub-target operation state of the equipment to be detected is determined by the corresponding target operation parameter;
determining the target running state of the equipment to be detected according to the plurality of sub-target running states of the equipment to be detected;
if the target running state is abnormal running, determining the fault position information of the equipment to be detected according to the position identifiers carried by all the sub-target running states of abnormal running.
Optionally, the inputting the plurality of target operation parameters to the target model to obtain a plurality of sub-target operation states includes:
inputting the multiple target operation parameters into the target model, and calling corresponding sub-target models based on parameter identifiers of each target operation parameter, wherein the parameter identifiers are in one-to-one correspondence with the sub-target models;
and obtaining the corresponding sub-target running state according to each target running parameter based on each sub-target model.
Optionally, the inputting the plurality of target operation parameters to the target model to obtain a plurality of sub-target operation states includes:
and respectively inputting the plurality of target operation parameters into a plurality of target models, and obtaining the sub-target operation state corresponding to each target operation parameter based on each target model, wherein the attributes of the plurality of target operation parameters are in one-to-one correspondence with the plurality of target models.
Optionally, the determining the target operation state of the device to be detected according to the plurality of sub-target operation states of the device to be detected includes:
if the plurality of sub-target running states are all normal running, determining that the target running states are normal running;
and if at least one sub-target running state in the plurality of sub-target running states is abnormal running, determining that the target running state is abnormal running.
Optionally, the method further comprises:
and remotely sending the target running state information to the mobile terminal.
Optionally, before the inputting the plurality of target operation parameters to the target model to obtain the target operation state information of the device to be detected, the method further includes:
inputting a plurality of operation parameters of the equipment to be detected in a normal operation state as input samples into an initial model to obtain output samples;
and according to the output sample and a preset running state, adjusting parameters of the initial model, returning to execute the operation of inputting a plurality of running parameters of the equipment to be detected in the normal running state as input samples into the initial model to obtain the output sample until a preset condition is met, and determining the initial model as the target model.
In a second aspect, the present disclosure provides a detection apparatus for a device, including:
the acquisition module is used for acquiring a plurality of target operation parameters of the equipment to be detected;
the determining module is used for inputting the plurality of target operation parameters into a target model to obtain target operation state information of the equipment to be detected, wherein the target operation state information comprises target operation state and fault position information.
In a third aspect, the present disclosure provides an electronic device comprising: a processor for executing a computer program stored in a memory, which when executed by the processor implements the steps of any of the methods provided in the first aspect.
In a fourth aspect, the present disclosure provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of any of the methods provided in the first aspect.
In the technical scheme provided by the disclosure, a plurality of target operation parameters of equipment to be detected are obtained; inputting a plurality of target operation parameters into a target model to obtain target operation state information of equipment to be detected, wherein the target operation state information comprises target operation state and fault position information, so that the current operation state of the equipment can be automatically detected based on the current operation parameters of the equipment, manual operation is not needed, and the detection efficiency of the equipment can be improved; in addition, the fault position of the equipment to be detected can be automatically obtained, manual troubleshooting is not needed, and the detection efficiency of the equipment can be further improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, the drawings that are required for the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flow chart of a detection method of a device provided in the present disclosure;
fig. 2 is a flow chart of another method for detecting a device provided in the present disclosure;
fig. 3 is a flow chart of a detection method of another device provided in the present disclosure;
fig. 4 is a flow chart of a detection method of another device provided in the present disclosure;
fig. 5 is a flow chart of a detection method of another device provided in the present disclosure;
fig. 6 is a flow chart of a detection method of another device provided in the present disclosure;
fig. 7 is a flow chart of a detection method of another device provided in the present disclosure;
fig. 8 is a schematic structural diagram of a detection device of an apparatus provided by the present disclosure.
Fig. 9 is a schematic structural diagram of an electronic device provided in the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
The technical solutions of the present disclosure are explained in detail below by means of several specific embodiments.
Fig. 1 is a flow chart of a detection method of an apparatus provided in the present disclosure, as shown in fig. 1, including:
s101, acquiring a plurality of target operation parameters of equipment to be detected.
The equipment to be detected can comprise a plurality of devices, and the operation parameters output by each device can be a plurality of or one, if the same device outputs different operation parameters, the different operation parameters of the device are used for reflecting different functional attributes of the device; if the different devices output the same operation parameters, the same operation parameters of the different devices are used for reflecting the same functional attributes of the different devices; if different devices output different operation parameters, the different operation parameters of the different devices are used for reflecting different functional attributes of the different devices.
For example, the device to be detected includes a camera and a sensor, the camera outputs an operation parameter A1 and an operation parameter A2, wherein the operation parameter A1 is used for reflecting whether an image acquisition function of the camera operates normally, and the operation parameter A2 is used for reflecting whether a data processing function of the camera operates normally. The sensor outputs an operation parameter A2 and an operation parameter A3, wherein the operation parameter A2 is used for reflecting whether the data processing function of the sensor is normally operated, and the operation parameter A3 is used for reflecting whether the data output function of the sensor is normally operated.
The plurality of target operation parameters of the equipment to be detected comprise all the operation parameters output by all the devices, and if the current operation state of the equipment to be detected needs to be detected, the plurality of target operation parameters of the equipment to be detected in the current operation state of the equipment to be detected need to be acquired first. The operation parameter herein may be at least one of voltage, current, and pressure, or may be at least one of voltage sequence, current sequence, pressure sequence, and pulse sequence, which is not particularly limited in this embodiment.
S103, inputting the plurality of target operation parameters into a target model to obtain target operation state information of the equipment to be detected.
The target running state information comprises target running state and fault position information.
The target model may be a ridge regression model, a logarithmic probability regression model, a random forest model, a long-term and short-term memory model, a neural network model, and the like. And inputting a plurality of target operation parameters into a target model, and outputting whether the current operation state of the equipment to be detected is normal operation or not by the target model aiming at the plurality of operation parameters in the current operation state. If the current running state of the equipment to be detected is normal running, considering that all the functional attributes of all the devices in the equipment to be detected are normal running; if the current running state of the equipment to be detected is abnormal running, at least one of all the functional attributes of all the devices in the equipment to be detected is considered to be abnormal running. In addition, under the condition that the current running state of the equipment to be detected is abnormal running, the fault position information of the equipment to be detected can be determined based on the target model.
For example, based on the above embodiment, the current plurality of target operation parameters of the device to be detected are the operation parameter A1, the operation parameter A2, and the operation parameter A3, respectively, and based on the operation parameter A1, the operation parameter A2, and the operation parameter A3, it may be determined that the current operation state of the device to be detected is normal operation; or, the current operation state of the equipment to be detected can be determined to be abnormal operation, and the fault position information of the equipment to be detected can be determined.
In this embodiment, a plurality of target operation parameters of the device to be detected are obtained; inputting a plurality of target operation parameters into a target model to obtain target operation state information of equipment to be detected, wherein the target operation state information comprises target operation state and fault position information, so that the current operation state of the equipment can be automatically detected based on the current operation parameters of the equipment, manual operation is not needed, and the detection efficiency of the equipment can be improved; in addition, the fault position of the equipment to be detected can be automatically obtained, manual troubleshooting is not needed, and the detection efficiency of the equipment can be further improved.
Fig. 2 is a flow chart of another method for detecting a device provided in the present disclosure, and fig. 2 is a specific description of one possible implementation manner when 103 is performed on the basis of the embodiment shown in fig. 1, as follows:
s1031, inputting the target operation parameters into the target model to obtain a plurality of sub-target operation states.
Each sub-target operating state of the device to be detected is determined by a corresponding target operating parameter.
The plurality of target operation parameters may be sequentially input to the target model, or the plurality of target operation parameters may be simultaneously input to the target model, and the target model may determine a current operation state of each functional attribute of each device in the apparatus to be detected, that is, a sub-target operation state, based on each input target operation parameter.
For example, based on the above embodiment, the operation parameter A1, the operation parameter A2, and the operation parameter A3 are input to the target model, and the target model may determine, for the operation parameter A1, the current operation state of the acquisition function of the camera, that is, the sub-operation state D1; the current running state of the data processing function of the camera, namely a sub-running state D2, can be determined according to one running parameter A2; the current operating state of the data processing function of the sensor, namely the sub-operating state D3, can be determined for the other operating parameter A2; the current operating state of the data output function of the sensor, namely the sub-operating state D4, can be determined for the operating parameter A3.
S1032, determining the target running state of the equipment to be detected according to the plurality of sub-target running states of the equipment to be detected.
Based on the above embodiment, the current operation state of the device to be detected is determined to be normal operation or abnormal operation according to the current operation state of the acquisition function of the camera, the current operation state of the data processing function of the sensor, and the current operation state of the data output function of the sensor.
S1033, if the target running state is abnormal running, determining fault position information of the equipment to be detected according to the position identifiers carried by all sub-target running states of abnormal running.
Each sub-target running state carries a position identifier, the position identifier is used for representing a functional attribute and/or a device corresponding to the sub-target running state in the equipment to be detected, and if the current running state of the equipment to be detected is determined to be abnormal running, the functional attribute and/or the device with faults in the equipment to be detected can be determined according to the position identifiers carried by all abnormal running in all sub-target running states.
For example, the sub-target operating state D1 is normal operation, the sub-target operating state D2 is normal operation, the sub-target operating state D3 is normal operation, and the sub-target operating state D4 is abnormal operation, based on the above embodiment, the location identifier of the sub-target operating state D4 is a data transmission function of the sensor, and then it may be determined that the current fault location of the device to be detected is in the data transmission module of the sensor.
In this embodiment, a plurality of target operation parameters are input to a target model to obtain a plurality of sub-target operation states, where each sub-target operation state of the device to be detected is determined by a corresponding target operation parameter; according to a plurality of sub-target operation states of the equipment to be detected, determining the target operation state of the equipment to be detected, if the target operation state is abnormal operation, determining the fault position information of the equipment to be detected according to the position identifiers carried by all the sub-target operation states of the abnormal operation, so that the operation states of components in the equipment can be determined, and the equipment can be comprehensively detected.
Fig. 3 is a flow chart of a detection method of another device provided in the present disclosure, and fig. 3 is a specific description of one possible implementation manner when S1031 is performed based on the embodiment shown in fig. 2, as follows:
s201, inputting the plurality of target operation parameters into the target model, and calling a corresponding sub-target model based on the parameter identification of each target operation parameter.
The parameter identifiers are in one-to-one correspondence with the sub-target models.
Each operation parameter corresponds to a parameter identifier, and in the process of training the target model, the input sample is the operation parameter with the parameter identifier, and the operation parameter with the parameter identifier is input into an initial model, so that the target model can be obtained. However, operating parameters based on different parameter identifications within the object model may result in a plurality of sub-object models, each sub-object model having a model identification. The target model aims at each input target operation parameter, finds out a matched model identifier from all model identifiers according to the parameter identifier of the target operation parameter, and calls a sub-target model corresponding to the matched model identifier.
For example, the target model includes four sub-target models, namely a sub-target model B1, a target model B2, a target model B3 and a target model B4, wherein the model of the sub-target model B1 is identified as M1, the model of the target model B2 is identified as M2, the model of the target model B3 is identified as M3, and the model of the target model B4 is identified as M4. The parameter of the operation parameter A1 is marked as m1, the parameter of one operation parameter A2 is marked as m2, the parameter of the other operation parameter A2 is marked as m3, and the parameter of the operation parameter A3 is marked as m4. Because the parameter identifier M1 is matched with the model identifier M1, the parameter identifier M2 is matched with the model identifier M2, the parameter identifier M3 is matched with the model identifier M3, and the parameter identifier M4 is matched with the model identifier M4, the sub-target model B1 is called for the operation parameter A1, the sub-target model B2 is called for the operation parameter A2 of the parameter identifier M2, the sub-target model B3 is called for the operation parameter A2 of the parameter identifier M3, and the sub-target model B4 is called for the operation parameter A3.
S202, based on each sub-target model, aiming at each target operation parameter, obtaining the corresponding sub-target operation state.
Illustratively, based on the above embodiment, the sub-target model B1 outputs the sub-target operating state D1 for the target operating parameter A1, the sub-target model B2 outputs the sub-target operating state D2 for the target operating parameter A2 of the parameter identification m2, the sub-target model B3 outputs the sub-target operating state D3 for the target operating parameter A2 of the parameter identification m3, and the sub-target model B4 outputs the sub-target operating state D4 for the target operating parameter A3.
In the embodiment, a plurality of target operation parameters are input into the target model, and corresponding sub-target models are called based on the parameter identification of each target operation parameter, wherein the parameter identification corresponds to the sub-target models one by one; based on each sub-target model, corresponding sub-target operation states are obtained for each target operation parameter, so that matched sub-target models can be called individually for different operation parameters to determine the sub-operation states, the accuracy of the sub-operation state results can be improved, and the accuracy of the detection results of the equipment can be improved. In addition, the time for model training can be shortened without separately training a plurality of target models.
Fig. 4 is a flow chart of a detection method of another device provided in the present disclosure, and fig. 4 is a specific description of another possible implementation manner when 1031 is performed based on the embodiment shown in fig. 2, as follows:
s1031', respectively inputting the plurality of target operation parameters into a plurality of target models, and obtaining the sub-target operation state corresponding to each target operation parameter based on each target model.
The attributes of the plurality of target operation parameters are in one-to-one correspondence with the plurality of target models.
One target model is trained based on a plurality of operation parameters corresponding to each functional attribute of each device, and thus, a plurality of target models can be trained based on a plurality of operation parameters corresponding to all functional attributes of all devices in the equipment to be detected. Each of the plurality of target operating parameters is input to a corresponding target model, and each target model can output a corresponding sub-target operating state for the input target operating parameter.
For example, the operation parameter A1 is input to the target model C1, the operation parameter A2 is input to the target model C2, another operation parameter A2 is input to the target model C3, and the operation parameter A3 is input to the target model C4. The target model C1 outputs a sub-target operating state D1 for the target operating parameter A1, the target model C2 outputs a sub-target operating state D2 for the target operating parameter A2, the target model C3 outputs a sub-target operating state D3 for the other target operating parameter A2, and the target model C4 outputs a sub-target operating state D4 for the target operating parameter A3.
In this embodiment, a plurality of target operation parameters are respectively input into a plurality of target models, and sub-target operation states corresponding to each target operation parameter are obtained based on each target model, and attributes of the plurality of target operation parameters are in one-to-one correspondence with the plurality of target models, so that the sub-operation states can be determined based on the matched target models for different operation parameters, the accuracy of the sub-operation state results can be improved, and the accuracy of the detection results of the equipment can be improved.
Fig. 5 is a flow chart of a detection method of another device provided in the present disclosure, and fig. 5 is a specific description of one possible implementation manner when implementing 1032 on the basis of the embodiment shown in fig. 2, as follows:
s301, determining whether all of the plurality of sub-target operation states are sub-target operation states of normal operation.
If yes, executing S302; if not, S303 is performed.
And determining whether the current running state of the equipment to be detected is normal running according to whether the current running state of all the functional attributes of all the devices in the equipment to be detected is normal running. For example, whether the current operation state of the device to be detected is normal can be determined according to whether all the sub-target operation states of the device to be detected are normal operation.
S302, determining the target running state as normal running.
If all sub-target running states of the equipment to be detected are normal running, namely all functional attributes of all devices in the equipment to be detected are normal running, the current running state of the equipment to be detected can be determined to be normal running. For example, based on the above embodiment, the sub-target operation state D1 is normal operation, the sub-target operation state D2 is normal operation, the sub-target operation state D3 is normal operation, and the sub-target operation state D4 is normal operation, then the current operation state of the device to be detected is normal operation.
S303, determining that the target running state is abnormal running.
If all the sub-target operation states of the equipment to be detected are not normal operation, that is, if one sub-target operation state exists in all the sub-target operation states, that is, if all the function attributes of all the devices in the equipment to be detected exist abnormal operation, the current operation state of the equipment to be detected can be determined to be abnormal operation. For example, based on the above embodiment, at least one of the sub-target operation state D1, the sub-target operation state D2, the sub-target operation state D3, and the sub-target operation state D4 is normal operation, and the current operation state of the device to be detected is abnormal operation.
Fig. 6 is a flow chart of a detection method of another device provided in the present disclosure, and fig. 6 is a flowchart of the embodiment shown in fig. 1, where the method further includes:
and S104, remotely sending the target running state information to the mobile terminal.
After the target running state and the fault position information of the equipment to be detected are determined, a preset user contact mode can be called, and the target running state and the fault information are sent to a remote mobile terminal of a user in a short message and/or telephone mode through a wireless network or a wired network, so that the user can receive the target running state and the fault position information of the equipment at any time and any place, and the maintenance of the user is facilitated.
Optionally, the target running state and the fault position information can also be sent to the equipment to be detected, so that the equipment to be detected generates corresponding alarm information based on the fault position information, and the user is prompted to maintain. The equipment to be detected can also display the target running state and fault position information on the display screen, so that the user can conveniently check and carry out targeted maintenance. The equipment to be detected can store the target running state and fault position information in a local terminal, so that a user can check historical faults conveniently.
In the embodiment, the target running state information is remotely sent to the mobile terminal, so that a user can receive the target running state and fault position information of the equipment at any time and any place, and the maintenance of the user is facilitated.
Fig. 7 is a flow chart of a detection method of another device provided in the present disclosure, and fig. 7 is a flowchart of the embodiment shown in fig. 1, before executing S103, further including:
s1021, a plurality of operation parameters of the equipment to be detected in a normal operation state are used as input samples to be input into an initial model, and an output sample is obtained.
Under the state that the equipment to be detected is in normal operation, a large number of operation parameters are obtained and used as input samples for model training, the input samples are input into an initial model, and the initial model outputs a corresponding output sample aiming at the input samples.
S1022, adjusting parameters of the initial model according to the output sample and a preset running state.
And taking the preset running state as a target output sample of the initial model to the input sample, wherein a difference exists between the output sample and the target output sample according to the output sample and the target output sample determined in the embodiment. This variability can be quantified as a loss value, the smaller the difference between the output sample and the target output sample, i.e., the higher the accuracy of the output result of the initial model, so that the parameters of the initial model are adjusted toward the loss value decreasing direction.
S1023, determining whether a preset condition is satisfied.
If not, executing S1021; if yes, execution proceeds to S1024.
And the output result of the initial model after parameter adjustment is closer to the target output sample than the output result of the initial model before adjustment after each iterative training. Thus, as the iterative training is accumulated, the loss value is smaller and smaller until the loss value or the iteration number meets the condition, and the training is finished.
S1024, determining the initial model as the target model.
Based on the above embodiment, the current initial model is considered to be the trained initial model when the preset condition is satisfied, and the trained initial model is determined to be the target model.
The disclosure further provides a detection device of an apparatus, and fig. 8 is a schematic structural diagram of the detection device of an apparatus provided by the disclosure, as shown in fig. 8, where the detection device includes:
the obtaining module 110 is configured to obtain a plurality of target operation parameters of the device to be detected.
The determining module 120 is configured to input the plurality of target operation parameters to a target model, and obtain target operation state information of the device to be detected, where the target operation state information includes target operation state and fault location information.
Optionally, the determining module 120 is further configured to input the plurality of target operation parameters to the target model to obtain a plurality of sub-target operation states, where each of the sub-target operation states of the device to be detected is determined by a corresponding target operation parameter; determining the target running state of the equipment to be detected according to the plurality of sub-target running states of the equipment to be detected; if the target running state is abnormal running, determining the fault position information of the equipment to be detected according to the position identifiers carried by all the sub-target running states of abnormal running.
Optionally, the determining module 120 is further configured to input the plurality of target operation parameters into the target model, and call a corresponding sub-target model based on a parameter identifier of each target operation parameter, where the parameter identifier corresponds to the sub-target model one to one; and obtaining the corresponding sub-target running state according to each target running parameter based on each sub-target model.
Optionally, the determining module 120 is further configured to input the plurality of target operation parameters into a plurality of target models, and obtain, based on each target model, the sub-target operation state corresponding to each target operation parameter, where attributes of the plurality of target operation parameters are in one-to-one correspondence with the plurality of target models.
Optionally, the determining module 120 is further configured to determine that the target operation state is normal operation if the plurality of sub-target operation states are all normal operations; and if at least one sub-target running state in the plurality of sub-target running states is abnormal running, determining that the target running state is abnormal running.
Optionally, the detection device further includes:
and the sending module is used for remotely sending the target running state information to the mobile terminal.
Optionally, the detection device further includes:
the training module is also used for inputting a plurality of operation parameters of the equipment to be detected in a normal operation state as input samples into the initial model to obtain output samples; and according to the output sample and a preset running state, adjusting parameters of the initial model, returning to execute the operation of inputting a plurality of running parameters of the equipment to be detected in the normal running state as input samples into the initial model to obtain the output sample until a preset condition is met, and determining the initial model as the target model.
The device provided in the present disclosure may be used to perform the steps of the above method embodiments, and its implementation principle and technical effects are similar, and are not described herein again.
Fig. 9 the present disclosure provides a schematic structural diagram of an electronic device, and fig. 9 shows a block diagram of an exemplary electronic device suitable for implementing the embodiment of the present invention. The electronic device shown in fig. 9 is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.
As shown in fig. 9, the electronic device 12 is in the form of a general purpose computing device. Components of the electronic device 12 may include, but are not limited to: one or more processors 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processors 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Electronic device 12 typically includes a variety of computer system readable media. Such media can be any medium that is accessible by electronic device 12 and includes both volatile and non-volatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (commonly referred to as a "hard disk drive"). Disk drives for reading from and writing to removable nonvolatile magnetic disks (e.g., a "floppy disk"), and optical disk drives for reading from and writing to removable nonvolatile optical disks (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The processor 16 executes various functional applications and data processing, such as implementing method embodiments provided by embodiments of the present invention, by running at least one of a plurality of programs stored in the system memory 28.
The present disclosure also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described method embodiments.
Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The present disclosure also provides a computer program product which, when run on a computer, causes the computer to perform the steps of implementing the method embodiments described above.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A method for detecting a device, comprising:
acquiring a plurality of target operation parameters of equipment to be detected;
inputting the plurality of target operation parameters into a target model to obtain target operation state information of the equipment to be detected, wherein the target operation state information comprises target operation state and fault position information;
the step of inputting the plurality of target operation parameters into a target model to obtain target operation state information of the equipment to be detected includes:
inputting the target operation parameters into the target model to obtain a plurality of sub-target operation states, wherein each sub-target operation state of the equipment to be detected is determined by the corresponding target operation parameter;
determining the target running state of the equipment to be detected according to the plurality of sub-target running states of the equipment to be detected;
if the target running state is abnormal running, determining the fault position information of the equipment to be detected according to the position identifiers carried by all the sub-target running states of abnormal running.
2. The method of claim 1, wherein said inputting the plurality of target operating parameters into the target model results in a plurality of sub-target operating states, comprising:
inputting the multiple target operation parameters into the target model, and calling corresponding sub-target models based on parameter identifiers of each target operation parameter, wherein the parameter identifiers are in one-to-one correspondence with the sub-target models;
and obtaining the corresponding sub-target running state according to each target running parameter based on each sub-target model.
3. The method of claim 1, wherein said inputting the plurality of target operating parameters into the target model results in a plurality of sub-target operating states, comprising:
and respectively inputting the plurality of target operation parameters into a plurality of target models, and obtaining the sub-target operation state corresponding to each target operation parameter based on each target model, wherein the attributes of the plurality of target operation parameters are in one-to-one correspondence with the plurality of target models.
4. A method according to any one of claims 1-3, wherein said determining said target operational state of said device to be detected from said plurality of sub-target operational states of said device to be detected comprises:
if the plurality of sub-target running states are all normal running, determining that the target running states are normal running;
and if at least one sub-target running state in the plurality of sub-target running states is abnormal running, determining that the target running state is abnormal running.
5. A method according to any one of claims 1-3, further comprising:
and remotely sending the target running state information to the mobile terminal.
6. A method according to any one of claims 1-3, wherein before inputting the plurality of target operating parameters into a target model to obtain the target operating state information of the device to be detected, further comprises:
inputting a plurality of operation parameters of the equipment to be detected in a normal operation state as input samples into an initial model to obtain output samples;
and according to the output sample and a preset running state, adjusting parameters of the initial model, returning to execute the operation of inputting a plurality of running parameters of the equipment to be detected in the normal running state as input samples into the initial model to obtain the output sample until a preset condition is met, and determining the initial model as the target model.
7. A device for detecting an apparatus, comprising:
the acquisition module is used for acquiring a plurality of target operation parameters of the equipment to be detected;
the determining module is used for inputting the plurality of target operation parameters into a target model to obtain target operation state information of the equipment to be detected, wherein the target operation state information comprises target operation state and fault position information;
the determining module is used for inputting the target operation parameters into the target model to obtain a plurality of sub-target operation states, and each sub-target operation state of the equipment to be detected is determined by the corresponding target operation parameter; determining the target running state of the equipment to be detected according to the plurality of sub-target running states of the equipment to be detected; if the target running state is abnormal running, determining the fault position information of the equipment to be detected according to the position identifiers carried by all the sub-target running states of abnormal running.
8. An electronic device, comprising: a processor for executing a computer program stored in a memory, which when executed by the processor carries out the steps of the method according to any one of claims 1-6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1-6.
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